Gamified Text Testing for Sustainable Fairness

Author:

Takan Savaş1ORCID,Ergün Duygu2ORCID,Katipoğlu Gökmen3

Affiliation:

1. Department of Artificial Intelligence and Data Engineering, Faculty of Engineering, Ankara University, 06830 Ankara, Turkey

2. School of Fine Arts Design and Architecture, Atılım University, 06830 Ankara, Turkey

3. Department of Computer Engineering, Faculty of Engineering, Kafkas University, 36100 Kars, Turkey

Abstract

AI fairness is an essential topic as regards its topical and social-societal implications. However, there are many challenges posed by automating AI fairness. Based on the challenges around automating fairness in texts, our study aims to create a new fairness testing paradigm that can gather disparate proposals on fairness on a single platform, test them, and develop the most effective method, thereby contributing to the general orientation on fairness. To ensure and sustain mass participation in solving the fairness problem, gamification elements are used to mobilize individuals’ motivation. In this framework, gamification in the design allows participants to see their progress and compare it with other players. It uses extrinsic motivation elements, i.e., rewarding participants by publicizing their achievements to the masses. The validity of the design is demonstrated through the example scenario. Our design represents a platform for the development of practices on fairness and can be instrumental in making contributions to this issue sustainable. We plan to further realize a plot application of this structure designed with the gamification method in future studies.

Publisher

MDPI AG

Subject

Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development,Building and Construction

Reference55 articles.

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3. Plane, A.C., Redmiles, E.M., Mazurek, M.L., and Tschantz, M.C. (2017, January 16–18). Exploring User Perceptions of Discrimination in Online Targeted Advertising. Proceedings of the 26th USENIX Security Symposium (USENIX Security 17), Vancouver, BC, Canada.

4. Machine learning fairness notions: Bridging the gap with real-world applications;Makhlouf;Inf. Process. Manag.,2021

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1. AI Fairness–From Machine Learning to Federated Learning;Computer Modeling in Engineering & Sciences;2024

2. Bias in human data: A feedback from social sciences;WIREs Data Mining and Knowledge Discovery;2023-04-20

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